Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours

Authors:
Yogesh Rathi;Namrata Vaswani;Allen Tannenbaum;Anthony Yezzi
Affiliations:
IEEE;-;-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2007

Citing 19
Cited 27

Algorithms

Algorithms
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Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects

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Real-Time Tracking Using Level Sets

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A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

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Parametric active contours for object tracking based on matching degree image of object contour points

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A Variational Technique for Time Consistent Tracking of Curves and Motion

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Cooperative Object Segmentation and Behavior Inference in Image Sequences

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Variance reduction techniques in particle-based visual contour tracking

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Motion Segmentation: a Review

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Robust contour tracking based on a coupling between geodesic active contours and conditional random fields

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Using incremental subspace and contour template for object tracking

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An adaptive sample count particle filter

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Abstract

Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen parametrization and cannot handle changes in curve topology. Geometric active contours provide a framework which is parametrization independent and allow for changes in topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects. To the best of our knowledge, this is the first attempt to implement an approximate particle filtering algorithm for tracking on a (theoretically) infinite dimensional state space.